Study analyzes costs of managing research funds, developing a model for optimal administration.
problem High variability in administration costs among research funding agencies.
method Identified standard agency activities, developed a model estimating optimum portfolio success rate and administration ratio.
result Model estimates optimum portfolio success rate and administration ratio based on input variables.
Equity-Directed Bootstrapping improves model performance across groups in imbalanced datasets.
problem Improving model performance across different groups in imbalanced datasets.
method Equity-Directed Bootstrapping to balance training data with respect to both labels and group identity.
result The equity-directed bootstrap brings test set sensitivities and specificities closer to satisfying the equal odds criterion.
Deep learning improves CVD risk prediction from health records.
problem Predicting cardiovascular disease risk from administrative health data.
method Combined survival analysis and deep learning models.
result Deep learning models outperform traditional Cox models in accuracy and explained time-to-event occurrence.
Germany's tax admin costs likely exceed 20% of total revenue, requiring system improvement.
problem High tax administrative costs in Germany and other jurisdictions.
method Statistical data, surveys, and a novel approach to measure total administrative cost as a percentage of total tax revenue.
result Germany's 2021 tax administrative costs likely exceeded 20% of total tax revenue.
New method uses geometric mean to avoid non-collapsibility in case-control studies.
problem Non-collapsibility of odds ratio under outcome-dependent sampling.
method Proposes geometric mean aggregation to avoid non-collapsibility and provides estimation and inference methods.
result Geometric odds ratio is collapsible under outcome-dependent sampling.
The paper analyzes fairness of compensation-based risk-sharing schemes for fund payouts.
problem Fair allocation of payouts in an endowment contingency fund.
method Analyzes two types of administrators and general non-negative loss distributions.
result General conditions for actuarial fairness are provided.
This paper provides a guide to using machine learning in public administration.
problem Lack of clarity in proper use and potential pitfalls of machine learning methods.
method Provides a foundational view of machine learning and demonstrates its use in public administration research.
result Machine learning techniques can enrich public administration research and practice.
New model maps malaria prevalence across Kenya's changing administrative boundaries.
problem Mapping disease prevalence with changing administrative boundaries.
method Combines deep learning and MCMC with aggVAE for disease mapping.
result Solves the change-of-support problem in disease surveillance.
This paper addresses issues with the Brier score in administrative censoring scenarios.
problem Problems with the Brier score in administrative censoring scenarios.
method Proposes an alternative Brier score for administratively censored data.
result The administrative Brier score is valid even when censoring times can be identified from covariates.
Study improves risk evaluation timing with right-censored reporting delays.
problem Improving risk evaluation under short observation windows due to administrative censoring.
method Jointly models parametric hazards for event and reporting processes, uses Monte Carlo expectation-maximization algorithm, and proposes transfer-learning procedure.
result Improves accuracy of timely risk evaluation under administrative censoring.
Study on tax administration issues and their impact on Georgia's budget revenues.
problem Problems in revenue administration and tax rates in Georgia.
method Analyzed foreign experience and proposed a progressive tax system.
result A progressive tax system would benefit Georgia's business and economy.
New method simplifies data analysis.
problem Complex data analysis challenges.
method Innovative algorithm for data simplification.
result Significant reduction in analysis time.
This article was withdrawn by the arXiv.org administrators since it plagiarizes math.AT/0401211.
This article was withdrawn by the arXiv.org administrators since it plagiarizes math.GT/0011056.
This paper has been withdrawn by arXiv administrators because of disputed claims of authorship among former collaborators
Policy shifts between Trump and Biden impact ESG investments, creating volatility.
problem Dramatic policy shifts between Trump and Biden administrations affect ESG investments.
method Analyzes contrasting policies of Trump and Biden administrations and their impacts on ESG investments.
result Policy changes significantly influence ESG investments, leading to volatility and portfolio reassessment.
Paper proposes a recursive PLS model for optimal response to security threats.
problem Optimal response to security threats after violations have occurred.
method Recursive Partial Least Squares (PLS) model with factorial analysis of security events.
result The model optimally estimates security administrators' responses to threats.
Dataset of Italian municipalities' income taxes from 2007-2011.
problem Understanding the economic structure of Italian municipalities.
method Annual aggregated income taxes of all Italian municipalities, clustered by regions and provinces.
result Data useful for economic comparisons and understanding municipal structures.
Optimizes quantum circuits using evolutionary strategies.
problem Optimizing quantum circuits for efficiency.
method Uses evolution strategies to optimize circuits.
result Improves quantum circuit performance.
We demonstrate by mathematical analysis and systematic computer simulations that redistribution can lead to sustainable growth in a society. The human capital dynamics of each agent is described by a stochastic multiplicative process which, in the long run, leads to the destruction of individual human capital and the e…
Accurate prediction of suicide risk in mental health patients remains an open problem. Existing methods including clinician judgments have acceptable sensitivity, but yield many false positives. Exploiting administrative data has a great potential, but the data has high dimensionality and redundancies in the recording …
Study shows Lula's Zero Hunger program reduced income inequality in Brazil.
problem Income inequality in Brazil during Lula's administration.
method Breakpoint regression analysis using detailed descriptive statistics.
result The Zero Hunger program substantially reduced income inequality and provided income security for the poor.
Modeling student course choices using latent variables.
problem Understanding student enrollment patterns in large universities.
method Probabilistic approach based on multilabel classification and mixture models.
result Demonstrated the model's ability to infer student interests guiding enrollment decisions.
NetBiTE predicts drug sensitivity and identifies biomarkers in cancer.
problem Predicting drug sensitivity and identifying biomarkers in cancer.
method NetBiTE combines prior knowledge and gene expression data using a biased tree ensemble approach.
result NetBiTE outperforms RF in predicting IC50 drug sensitivity for drugs targeting membrane receptor pathways.
Framework for fast CAT calibration and administration using AutoML and IRT.
problem Calibrating and administering large-scale CAT tests with limited data.
method AutoIRT (AutoML + IRT) for calibration, BanditCAT for administration.
result Framework successfully launched new item types on DET practice test.
Study uses data to analyze COPD patients' impact on hospital systems.
problem Understanding and quantifying resource requirements for COPD patients.
method Combines segmentation, queuing theory, and data recovery techniques.
result Finding useful operational results from incomplete administrative data.
Paper proposes a method to estimate confidence bands for survival random forests.
problem No statistically valid and computationally feasible approach for estimating confidence bands for survival random forests.
method Extending recent developments in infinite-order incomplete U-statistics, the paper proposes an unbiased confidence band estimation.
result The proposed method accurately estimates the confidence band and achieves desired coverage rate.
Neural networks outperform logistic regression for predicting HF readmission.
problem Predicting 30-day all-cause readmission in heart failure patients.
method Used a large administrative claims dataset to compare neural network models (RNNCRF) with logistic regression models (LASSO) for predicting readmission.
result RNNCRF model achieved best performance with 0.642 AUC, while logistic regression with LASSO had equal performance.
Study predicts high school dropout risk in Louisiana using imbalanced learning techniques.
problem Predicting high school dropout risk in Louisiana.
method Applied imbalanced learning techniques including resampling, case weighting, and cost-sensitive learning.
result Imbalanced learning techniques improve recall but decrease precision.
Method fuses low and high-resolution data for better health estimates.
problem Improving high-resolution health estimates from mixed data sources.
method Fusion of unbiased low-resolution and potentially biased high-resolution data, learning a distribution consistent with sampling bias.
result Significant reduction in bias in high-resolution estimates.
The paper learns personalized treatment rules from observational data.
problem Developing effective treatment policies for individual patients.
method Contextual bandit approach to minimize expected risk of treatment policies.
result The proposed method outperforms physicians and baseline approaches in IV and VP administration.
Interpretable neural networks improve economic research by balancing accuracy and transparency.
problem Lack of interpretability in neural networks hinders their use in economic research.
method Proposes interpretable neural network models that balance prediction accuracy and interpretability.
result Achieved 94.5% accuracy in predicting employment status using high-dimensional data.
The study examines how alternative resource adequacy contract designs affect market participants' risk profiles and resource mix.
problem The tension between promoting reliability and competition in liberalized electricity markets.
method Constructs a stochastic equilibrium model of a competitive market with incomplete risk trading and computes investment equilibria under different contracting regimes.
result Alternative contracting regimes can induce different risk profiles and resource mixes, affecting market outcomes.
Paper uses 2-step Gradient Boosting to predict VAT tax gap.
problem Estimating tax evasion and revenue loss from tax avoidance.
method 2-steps Gradient Boosting model to correct selection bias.
result Significantly improved prediction of VAT tax gap.
Modeling disease progression in irregularly observed patients.
problem Irregular patient observation in healthcare databases.
method Continuous-time hidden Markov model with generalized linear model.
result Interpretable model of healthcare utilization events.
A2A metric evaluates bias correction methods, reducing ATE estimation errors.
problem Selection biases in non-randomized studies of medical treatments.
method Propensity score matching (PSM) with novel metric A2A.
result Reduces ATE estimation errors by up to 90% across synthetic and real-world datasets.
This paper explains tax policy for crypto assets in a rapidly evolving tech landscape.
problem Rapid technological changes in crypto assets create regulatory and tax policy blind spots.
method Explains principles of crypto assets, their technology, and tax issues.
result Tax policies are lagging behind innovation in blockchain and crypto.
This study simulates the evolution of artificial economies in order to understand the tax relevance of administrative boundaries in the quality of life of its citizens. The modeling involves the construction of a computational algorithm, which includes citizens, bounded into families; firms and governments; all of them…
REP predicts drug response at every stage of treatment using time-course gene expression data.
problem Lack of dynamic drug response prediction from time-course gene expression data.
method REP framework that predicts drug response values at every stage of a long-term treatment using recursive structure and tensor completion.
result REP can estimate drug response at any stage of a given treatment from initial gene expression levels.
The Fano ratio offers a more diversified portfolio than the Sharpe ratio in optimization.
problem Optimizing portfolio performance using the Sharpe ratio.
method Introducing and optimizing the Fano ratio as an alternative to the Sharpe ratio.
result Optimizing for the Fano ratio leads to more diversified and less skewed portfolios.
This article presents results from the first statistically significant study of cost escalation in transportation infrastructure projects. Based on a sample of 258 transportation infrastructure projects worth US$90 billion and representing different project types, geographical regions, and historical periods, it is fou…
New method to decompose portfolio performance ratios.
problem Understanding the drivers of portfolio performance ratios.
method Using Euler's theorem, decomposes performance ratios into modified ratios.
result Derives condition for new asset to improve portfolio performance.
Omega ratio is shown to be equivalent to Sharpe ratio under certain distributional assumptions.
problem Comparing Omega ratio to Sharpe ratio as performance indicators.
method Computation and analysis of Omega ratio for normal distribution and proof for elliptic distributions.
result Omega ratio is equivalent to Sharpe ratio for returns with elliptic distributions.
Study predicts which patients will benefit from digital health interventions.
problem Unclear targeting of patients for digital care management programs.
method Analyzed claims data, combined with sociodemographic and app-generated data. Created two models: cost prediction and impactability classification.
result Random forest model accurately categorized patients as impactable or not, achieving 71.9% accuracy.
FORE evaluates occupancy ratios without requiring Bellman completeness.
problem Offline reinforcement learning occupancy ratio estimation.
method Fitted occupancy-ratio evaluation (FORE) using adjoint Bellman recursion.
result FORE achieves convergence in KL without Bellman completeness.
Optimal option portfolios under Sharpe Ratio maximization with skew-elliptical t-distributed returns
problem Optimal option portfolios under Sharpe Ratio maximization
method Formulation for explicit portfolio weights
result Different optimal portfolios for Sharpe Ratio and return-to-Value-at-Risk (VaR) ratio
Study evaluates training programs for unemployed in Belgium using machine learning.
problem Determining which training programs are most effective for unemployed individuals in Belgium.
method Used Modified Causal Forests, a causal machine learning estimator, to analyze data from unemployed in Belgium.
result There is significant heterogeneity in the effectiveness of different training programs for unemployed individuals in Belgium.
We investigate relationship between annual electric power consumption per capita and gross domestic production (GDP) per capita for 131 countries. We found that the relationship can be fitted with a power-law function. We examine the relationship for 47 prefectures in Japan. Furthermore, we investigate values of annual…